Abstract
This paper reviews current developments and discusses some critical issues with obstacle detection systems for automated vehicles. The concept of autonomous driving is the driver towards future mobility. Obstacle detection systems play a crucial role in implementing and deploying autonomous driving on our roads and city streets. The current review looks at technology and existing systems for obstacle detection. Specifically, we look at the performance of LIDAR, RADAR, vision cameras, ultrasonic sensors, and IR and review their capabilities and behaviour in a number of different situations: during daytime, at night, in extreme weather conditions, in urban areas, in the presence of smooths surfaces, in situations where emergency service vehicles need to be detected and recognised, and in situations where potholes need to be observed and measured. It is suggested that combining different technologies for obstacle detection gives a more accurate representation of the driving environment. In particular, when looking at technological solutions for obstacle detection in extreme weather conditions (rain, snow, fog), and in some specific situations in urban areas (shadows, reflections, potholes, insufficient illumination), although already quite advanced, the current developments appear to be not sophisticated enough to guarantee 100% precision and accuracy, hence further valiant effort is needed.
Highlights
According to the World Health Organisation, in 2015, there was a total of 1.25 million traffic accidents, 270,000 people fatalities, resulting in over 700 life losses each day on average [1]
Governments, car manufacturers and municipal departments have considered a large amount of investment to support the development of various technological solutions, including autonomous driving and cognitive robotics, where around 1 billion euros have already been invested by EU agencies [3]
The image captured by human eye when perceiving the world can contain several GB of information with a single blink if converted from an abstract scale into a computing number for easier comparison, while the latest generation obstacle detection system is only able to deal with MB data in a short period
Summary
According to the World Health Organisation, in 2015, there was a total of 1.25 million traffic accidents, 270,000 people fatalities, resulting in over 700 life losses each day on average [1]. Discussions on how different technologiessolutions perform in situations by during daytimeon or at night advances limitations of shadows, existing systems, including visual cameras, SONAR and whenand dealing with, e.g., colours, and reflections from smooth LIDAR, surfaces,RADAR, are presented. Suitable for to accurately read road signals and colour buttons, but are limited by the visibility conditions parking assistant in parking lots due to its fast response in a relatively short range. Data collected from different technologies is not homogeneous; as a result, a sophisticated data fusion mechanism is needed for accurate data analytics In both extreme weather conditions (rain, snow, fog) and some specific situations in urban areas, quite advanced, the current technological developments are not sophisticated enough to guarantee 100% precision and accuracy of obstacle detection.
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